Stability of Hybrid Maize Reaction to Gibberella Ear Rot and Deoxynivalenol Contamination of Grain

Trials were conducted to quantify the stability (or lack of G × E interaction) of 15 maize hybrids to Gibberella ear rot (GER; caused by Fusarium graminearum) and deoxynivalenol (DON) contamination of grain across 30 Ohio environments (3 years × 10 locations). In each environment, one plot of each hybrid was planted and 10 ears per plot were inoculated via the silk channel. GER severity (proportion of ear area diseased) and DON contamination of grain (ppm) were quantified. Multiple rank-based methods, including Kendall’s concordance coefficient (W) and Piepho’s U, were used to quantify hybrid stability. The results found insufficient evidence to suggest crossover G × E interaction of ranks, with W greater than zero for GER (W = 0.28) and DON (W = 0.26), and U not statistically significant for either variable (P > 0.20). Linear mixed models (LMMs) were also used to quantify hybrid stability, accounting for crossover or noncrossover G × E interaction of transformed observed data. Based on information criteria and likelihood ratio tests for GER and DON response variables, the models with more complex variance-covariance structures—heterogeneous compound symmetry and factor-analytic—provided a better fit than the model with the simpler compound symmetry structure, indicating that one or more hybrids differed in stability. Overall, hybrids were stable based on rank-based methods, which indicated a lack of crossover G × E interaction, but the LMMs identified a few hybrids that were sensitive to environment. Resistant hybrids were generally more stable than susceptible hybrids.

A Theory of Statistical Inference for Matching Methods in Causal Research

Researchers who generate data often optimize efficiency and robustness by choosing stratified over simple random sampling designs. Yet, all theories of inference proposed to justify matching methods are based on simple random sampling. This is all the more troubling because, although these theories require exact matching, most matching applications resort to some form of ex post stratification (on a propensity score, distance metric, or the covariates) to find approximate matches, thus nullifying the statistical properties these theories are designed to ensure. Fortunately, the type of sampling used in a theory of inference is an axiom, rather than an assumption vulnerable to being proven wrong, and so we can replace simple with stratified sampling, so long as we can show, as we do here, that the implications of the theory are coherent and remain true. Properties of estimators based on this theory are much easier to understand and can be satisfied without the unattractive properties of existing theories, such as assumptions hidden in data analyses rather than stated up front, asymptotics, unfamiliar estimators, and complex variance calculations. Our theory of inference makes it possible for researchers to treat matching as a simple form of preprocessing to reduce model dependence, after which all the familiar inferential techniques and uncertainty calculations can be applied. This theory also allows binary, multicategory, and continuous treatment variables from the outset and straightforward extensions for imperfect treatment assignment and different versions of treatments.

USING COVARIANCE MATRIX FOR CHANGE DETECTION OF POLARIMETRIC SAR DATA

Nowadays change detection is an important role in civil and military fields. The Synthetic Aperture Radar (SAR) images due to its independent of atmospheric conditions and cloud cover, have attracted much attention in the change detection applications. When the SAR data are used, one of the appropriate ways to display the backscattered signal is using covariance matrix that follows the Wishart distribution. Based on this distribution a statistical test for equality of two complex variance-covariance matrices can be used. In this study, two full polarization data in band L from UAVSAR are used for change detection in agricultural fields and urban areas in the region of United States which the first image belong to 2014 and the second one is from 2017. To investigate the effect of polarization on the rate of change, full polarization data and dual polarization data were used and the results were compared. According to the results, full polarization shows more changes than dual polarization.

Mathematical Conjecture for Random Complex Distributions of Random Complex Variables and their Common Statistics

<p><span style="font-family: Times New Roman; font-size: medium;">The random complex variable is a kind of random variable which in complex number as the random variable. In this paper, study on mathematical conjecture of random complex variable, two-dimensional joint distribution and complex expected, complex variance and other common statistics in the real and imaginary part of the random variables satisfy the independent standard normal distribution condition.</span></p>

Formula for success: Multilevel modelling of Formula One Driver and Constructor performance, 1950–2014

This paper uses random-coefficient models and (a) finds rankings of who are the best formula 1 (F1) drivers of all time, conditional on team performance; (b) quantifies how much teams and drivers matter; and (c) quantifies how team and driver effects vary over time and under different racing conditions. The points scored by drivers in a race (standardised across seasons and Normalised) is used as the response variable in a cross-classified multilevel model that partitions variance into team, team-year and driver levels. These effects are then allowed to vary by year, track type and weather conditions using complex variance functions. Juan Manuel Fangio is found to be the greatest driver of all time. Team effects are shown to be more important than driver effects (and increasingly so over time), although their importance may be reduced in wet weather and on street tracks. A sensitivity analysis was undertaken with various forms of the dependent variable; this did not lead to substantively different conclusions. We argue that the approach can be applied more widely across the social sciences, to examine individual and team performance under changing conditions.

Variability of responses to sinusoidal modulation

Many studies of visual neurons make use of stimuli that are sinusoidally modulated in time, and take as the response the fundamental Fourier component of the firing. This is a study of the variability of the fundamental sinusoidal components.A theoretical analysis shows that the variance of sinusoidal components should be nearly independent of their amplitudes; this is expected despite the observation that variance of firing rate increases with increasing firing rate. However, this result applies only to the variance of the complex amplitude, defined as the complex Fourier amplitude in response to each stimulus cycle. This variance is called the complex variance. The variance of the scalar amplitude, which is simply the amplitude in response to each stimulus cycle disregarding phase (scalar variance) is expected to shrink by a factor of up to 2⅓ as the response magnitude approaches zero.If the relationship between variance of rate and rate is linear, complex variance should be independent of amplitude. If the relationship between variance of rate and rate is characterized by a compressive nonlinearity (as has been observed), the complex variance should very slightly decrease with increased amplitude, despite the main trend of increased variance of rate with increased rate.Data from cat ganglion cells stimulated with sinusoidally modulated lights of various contrasts agree with the theory, although some individual cases show trends that may be indicative of nonlinearity in the relationship between variance of rate and rate.